code: def in_backprop(tensor, interface=None):
      """Returns True if the tensor is considered to be in a backpropagation environment, it works for Autograd,
      Tensorflow and Jax. It is not only checking the differentiability of the tensor like :func:`~.requires_grad`, but
      rather checking if the gradient is actually calculated.
  
      Args:
          tensor (tensor_like): input tensor
          interface (str): The name of the interface. Will be determined automatically
              if not provided.
  
      **Example**
  
      >>> x = tf.Variable([0.6, 0.1])
      >>> requires_grad(x)
      False
      >>> with tf.GradientTape() as tape:
      ...     print(requires_grad(x))
      True
  
      .. seealso:: :func:`~.requires_grad`
      """
      interface = interface or get_interface(tensor)
  
      if interface == "tensorflow":
          import tensorflow as tf
  
          should_record_backprop = import_should_record_backprop()
          return should_record_backprop([tf.convert_to_tensor(tensor)])
  
      if interface == "autograd":
          return isinstance(tensor, ArrayBox)
  
      if interface == "jax":
          import jax
  
          return isinstance(tensor, jax.core.Tracer)
  
      if interface in {"numpy", "scipy"}:
          return False
  
      raise ValueError(
          f"Cannot determine if {tensor} is in backpropagation."
      )
  
file_path: pennylane/pennylane/math/utils.py
imports: import warnings
  import autoray
  import numpy
  from autograd.numpy.numpy_boxes import ArrayBox
  from autoray import numpy
  from .None import single_dispatch
label_desc: Use image node_id 12 for calling a global function with example usage: in_backprop(tensor, interface) and returns: should_record_backprop, isinstance, isinstance, False